Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Observation of Magnetic Moments in the Superconducting State of YBa2_2Cu3_3O6.6_{6.6}

Neutron Scattering measurements for YBa$_2$Cu$_3$O$_{6.6}$ have identified small magnetic moments that increase in strength as the temperature is reduced below $T^\ast$ and further increase below $T_c$. An analysis of the data shows the moments are antiferromagnetic between the Cu-O planes with a correlation length of longer than 195 \AA in the $a$-$b$ plane and about 35 \AA along the c-axis. The origin of the moments is unknown, and their properties are discusssed both in terms of Cu spin magnetism and orbital bond currents.Comment: 9 pages, and 4 figure

The connection between superconducting phase correlations and spin excitations in YBa2_2Cu3_3O6.6_{6.6}: A magnetic field study

One of the most striking universal properties of the high-transition-temperature (high-$T_c$) superconductors is that they are all derived from the hole-doping of their insulating antiferromagnetic (AF) parent compounds. From the outset, the intimate relationship between magnetism and superconductivity in these copper-oxides has intrigued researchers. Evidence for this link comes from neutron scattering experiments that show the unambiguous presence of short-range AF correlations (excitations) in cuprate superconductors. Even so, the role of such excitations in the pairing mechanism and superconductivity is still a subject of controversy. For YBa$_2$Cu$_3$O$_{6+x}$, where $x$ controls the hole-doping level, the most prominent feature in the magnetic excitations spectra is the ``resonance''. Here we show that for underdoped YBa$_2$Cu$_3$O$_{6.6}$, where $x$ and $T_c$ are below the optimal values, modest magnetic fields suppress the resonance significantly, much more so for fields approximately perpendicular rather than parallel to the CuO$_2$ planes. Our results indicate that the resonance measures pairing and phase coherence, suggesting that magnetism plays an important role in the superconductivity of cuprates. The persistence of a field effect above $T_c$ favors mechanisms with preformed pairs in the normal state of underdoped cuprates.Comment: 12 pages, 4 figures, Nature (in press

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England

Hyperon semileptonic decays and quark spin content of the proton

We investigate the hyperon semileptonic decays and the quark spin content of the proton $\Delta \Sigma$ taking into account flavor SU(3) symmetry breaking. Symmetry breaking is implemented with the help of the chiral quark-soliton model in an approach, in which the dynamical parameters are fixed by the experimental data for six hyperon semileptonic decay constants. As a result we predict the unmeasured decay constants, particularly for $\Xi^0 \to \Sigma^+$, which will be soon measured and examine the effect of the SU(3) symmetry breaking on the spin content $\Delta \Sigma$ of the proton. Unfortunately large experimental errors of $\Xi^-$ decays propagate in our analysis making $\Delta \Sigma$ and $\Delta s$ practically undetermined. We conclude that statements concerning the values of these two quantities, which are based on the exact SU(3) symmetry, are premature. We stress that the meaningful results can be obtained only if the experimental errors for the $\Xi$ decays are reduced.Comment: The final version accepted for publication in Phys. Rev. D. 18 pages, RevTex is used with 4 figures include

Optically induced coherent intra-band dynamics in disordered semiconductors

On the basis of a tight-binding model for a strongly disordered semiconductor with correlated conduction- and valence band disorder a new coherent dynamical intra-band effect is analyzed. For systems that are excited by two, specially designed ultrashort light-pulse sequences delayed by tau relatively to each other echo-like phenomena are predicted to occur. In addition to the inter-band photon echo which shows up at exactly t=2*tau relative to the first pulse, the system responds with two spontaneous intra-band current pulses preceding and following the appearance of the photon echo. The temporal splitting depends on the electron-hole mass ratio. Calculating the population relaxation rate due to Coulomb scattering, it is concluded that the predicted new dynamical effect should be experimentally observable in an interacting and strongly disordered system, such as the Quantum-Coulomb-Glass.Comment: to be published in Physical Review B15 February 200

Probing superconducting phase fluctuations from the current noise spectrum of pseudogaped metal-superconductor tunnel junctions

We study the current noise spectra of a tunnel junction of a metal with strong pairing phase fluctuation and a superconductor. It is shown that there is a characteristic peak in the noise spectrum at the intrinsic Josephson frequency $\omega_J=2eV$ when $\omega_J$ is smaller than the pairing gap but larger than the pairing scattering rate. In the presence of an AC voltage, the tunnelling current noise shows a series of characteristic peaks with increasing DC voltage. Experimental observation of these peaks will give direct evidence of the pair fluctuation in the normal state of high-$T_c$ superconductors and from the half width of the peaks the pair decay rate can be estimated.Comment: 4 pages, 3 figure

Formation of sp³ bonding in nanoindented carbon nanotubes and graphite

Author name used in this publication: C. H. Woo2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Urinary biomarker concentrations of captan, chlormequat, chlorpyrifos and cypermethrin in UK adults and children living near agricultural land

There is limited information on the exposure to pesticides experienced by UK residents living near agricultural land. This study aimed to investigate their pesticide exposure in relation to spray events. Farmers treating crops with captan, chlormequat, chlorpyrifos or cypermethrin provided spray event information. Adults and children residing ≤100 m from sprayed fields provided first-morning void urine samples during and outwith the spray season. Selected samples (1–2 days after a spray event and at other times (background samples)) were analysed and creatinine adjusted. Generalised Linear Mixed Models were used to investigate if urinary biomarkers of these pesticides were elevated after spray events. The final data set for statistical analysis contained 1518 urine samples from 140 participants, consisting of 523 spray event and 995 background samples which were analysed for pesticide urinary biomarkers. For captan and cypermethrin, the proportion of values below the limit of detection was greater than 80%, with no difference between spray event and background samples. For chlormequat and chlorpyrifos, the geometric mean urinary biomarker concentrations following spray events were 15.4 μg/g creatinine and 2.5 μg/g creatinine, respectively, compared with 16.5 μg/g creatinine and 3.0 μg/g creatinine for background samples within the spraying season. Outwith the spraying season, concentrations for chlorpyrifos were the same as those within spraying season backgrounds, but for chlormequat, lower concentrations were observed outwith the spraying season (12.3 μg/g creatinine). Overall, we observed no evidence indicative of additional urinary pesticide biomarker excretion as a result of spray events, suggesting that sources other than local spraying are responsible for the relatively low urinary pesticide biomarkers detected in the study population

Evidence for coexistence of the superconducting gap and the pseudo - gap in Bi-2212 from intrinsic tunneling spectroscopy

We present intrinsic tunneling spectroscopy measurements on small Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ mesas. The tunnel conductance curves show both sharp peaks at the superconducting gap voltage and broad humps representing the $c$-axis pseudo-gap. The superconducting gap vanishes at $T_c$, while the pseudo-gap exists both above and below $T_c$. Our observation implies that the superconducting and pseudo-gaps represent different coexisting phenomena.Comment: 5 pages, 4 figure